Basic Knowledge

Sensitive Data

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|May 24, 2023

Sensitive Data: How to protect it?


  • Sensitive data is information that must be protected against unauthorized access.
  • Three aspects of measuring data sensitivity are confidentiality, integrity and availability.
  • There are 4 data classifications based on the sensitivity level: public, internal, private, and restricted.
  • What is sensitive data?

    Sensitive data is confidential data that can only be accessed by authorized users with proper clearance, privileges, or permission to view, process, or manage it on behalf of an individual or organization.

    How to Measure Data Sensitivity

    A common way to measure data sensitivity and classify it is to think about confidentiality, integrity, and availability (CIA) and what impact it would have on your organization if that data was leaked or exposed.


    Confidentiality is roughly equivalent to privacy. It means to inhibit unauthorized access to sensitive data.


    Integrity refers to the accuracy and trustworthiness of data over its lifecycle. Data with high sensitivity must not be edited by people with no authorization or access.


    Availability means the data is available when needed by authorized individuals or parties.

    Data Classification

    In order to protect the data, first, the organization must classify the data based on their sensitivity because not all data require the same security methods. The more sensitive the data is, the stricter restrictions on people who can access it. There are four data classifications as follows:


    This type of data can be shared with the open public. It has low sensitivity and does not need to be encrypted or require strict protection methods.


    This kind of data is not open to public view but also has low-security requirements. An example of internal data is a company’s sales playbook or organizational chart. Even though internal data should not be shared outside the organization’s environment, unauthorized disclosure of this data will not have serious non-compliance consequences.

    Confidential or Private

    This type of data has a moderate sensitivity level. If this data is disclosed, it will cause a moderate risk to the person or organization affected.


    Restricted data has the highest sensitivity level. The organization has to deal with a lot of financial, legal, regulatory and reputational risks if this data is compromised.

    Examples of Sensitive Data

    Here are some typical examples of sensitive data.

    Sensitive data can include financial information and personal data – Source: archTIS

    Customer Information

    Customer information refers to customer personal information such as phone number, address, transaction details, etc. If this data gets compromised, the customer will be affected, and the relationship between the customer and the organization will also be broken.

    Financial data

    This data is considered one of the most sensitive data. Financial information can be credit card numbers, bank account information, and social security numbers. Having this information in hand, the hacker can cause severe damage by withdrawing the money from the victim’s account, conducting unwanted transactions, and transferring the money without the victim’s acknowledgement.

    Personal Data

    Personal data is the information used to identify an individual. Examples of personal data are as follows:

  • Full name
  • Phone number
  • Identity number
  • Driver license number
  • Business information

    Confidential business data can be accounting data, trade secrets, financial statements or accounts, and any sensitive information in business plans.

    How to Protect Sensitive Data

    Almost everyone can imagine the consequences of highly sensitive data getting compromised. Here are steps your organization needs to take to strengthen the security measures and also provide flexibility with access management.

    Organize & Classify Data

    Data classification helps to categorize the data based on sensitivity level, and from that classification, the organization can implement suitable security measures. A data classification system will help your organization apply private policies and security measures better for highly sensitive data.

    Enable Data Encryption

    Data encryption is one of the most common ways to protect sensitive data. The data will be encrypted using algorithms. In case the data is compromised or stolen, it would be extremely challenging for the attackers to decode the data if they don’t have a decryption key. As a result, data encryption is used widely, with sensitive data being transmitted between partners and parties.
    Data encryption enables authentication processes to take place. Data with high sensitivity levels, such as financial or cardholder information, is usually always encrypted before being transmitted or sent. Nevertheless, data encryption is not 100% secure because of cryptographic attacks or the use of cloud storage. Encryption should be used in conjunction with many other protection measures to boost the security level.

    Perform Data Protection Impact Assessments (DPIA)

    To help organizations protect personal information from being exposed, Data Protection Impact Assessments (DPIAs) serve as valuable tools. Basically, DPIAs require organizations to:

  • Determine the extent, nature, purpose, and context of data processing.
  • Evaluate the risks involved for each individual or group.
  • Determine the security measures necessary and proportionate to address potential risks.
  • Ensure that security measures and processes comply with regulations.
  • Use Data Obfuscation

    Data obfuscation, or Data masking, is similar to data encryption, but the main difference is it replaces the initial data with fictional data through character shuffling or character substitution to strengthen the security level. The aim is to create a data version that can’t be decoded or reverse-engineered.

    The difference between data masking and data encryption – Source: Imperva

    One major benefit of data masking is preventing the organization’s employees from viewing and accessing sensitive data. Data masking can solve the problems of internal and external threats because it makes the data irrelevant and useless to the attacker.

    Set Up Multi-Factor Authentication

    Multi-factor authentication allows users to use an extra step of authentication method, such as confirming through a mobile app or SMS, besides routine login procedures. This requires more effort not only from the business side but also from the user side to turn on the multi-factor authentication and conduct an extra step to login.

    Implement Stronger Network Security

    Network security refers to various security solutions to protect sensitive data from being compromised. Here are some examples of tools to create a more secure network environment:

  • Antivirus & anti-malware software
  • Firewalls
  • Virtual private networks (VPN)
  • Data loss prevention (DLP)
  • Intrusion detection systems (IDS) & intrusion prevention systems (IPS)
  • Create Data Backups

    Data backup becomes incredibly useful in the event of malware or cyberattack. The organization can restore the data from the backup to mitigate the damage. The backup frequency should be somewhere once a week or more to prepare best for malicious cyberattack attempts.

    Final thoughts

    Every organization stores sensitive data, and with the increasing prevalence of cyberattacks nowadays, companies need to classify the data and strengthen the security measures for highly sensitive data.


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